Experimental design has a long tradition in statistics, engineering and life sciences, dating back to the beginning of the last century when optimal designs for industrial and agricultural trials were considered. In cell biology, the use of mathematical modeling approaches raises new demands on experimental planning. A maximum informative investigation of the dynamic behavior of cellular systems is achieved by an optimal combination of stimulations and observations over time. In this minireview, the existing approaches concerning this optimization for parameter estimation and model discrimination are summarized. Furthermore, the relevant classical aspects of experimental design, such as randomization, replication and confounding, are reviewed.